Various modes of telecommunication have not only revolutionized the way we communicate, but in today's world have also changed the way we do business, and the way we live our lives. Today, various modes of telecommunication are increasingly used to perform various functions such as streaming multimedia content, playing high definition online games, enabling video calls, and so forth in addition to basic voice calls. Each of these functions and other functions may require network resources. It may be therefore important to efficiently and effectively charge (i.e., bill) such communication sessions.
Existing techniques may typically provide for static charging for a communication session (i.e., charging using static policies/rules) based on a variety of factors such as time of day (e.g., day, night, etc.), session type (e.g., local call, ISD, etc.), characteristics of session (e.g., video call, etc.), resources required (e.g., bandwidth, volume of data, etc.), class of user (premium user, normal user, etc.), and so forth. However, static charging techniques may have certain limitations. For example, operator may lose out on the revenue in case of network congestion, for ensuring resource availability for the initiated session-maintenance and for taking steps to ensure desired service quality. Conversely, user may be compelled to pay undue charges to the operator even in case of service interruptions and receipt of degraded service quality.
Some of the existing techniques may provide for dynamic charging based on the network load conditions and the resources requested by the end-user, However, existing dynamic charging techniques may have certain limitations. For example, existing dynamic charging techniques may propose for estimation of the charging rate based on network load conditions at the start of the session, and for updating of the charging rate during the session based on periodic assessment of network load condition, which in turn may be based on periodic information received from network elements. However, initial charge estimation based on instantaneous network load conditions and resource availability may be inaccurate (e.g., over-estimation in case of overloaded network, under-estimation in case of under-loaded network, etc.). Further, frequent load reporting by the network elements may result in processing and transmission overheads. If the frequency of reporting is decreased (i.e., reporting at longer interval), some of the charging related conditions/events may get missed out, leading to inaccurate determination of charging, thereby defeating the purpose of determining charging based on dynamic network load conditions during the session. These issues may get further aggravated when the network congestion level is high due to load or other factors. Thus, existing dynamic charging techniques may result in inappropriate determination of charging-rate at the service-session initiation and during the service-session. Also, the processing and transmission overhead may impact service quality for that particular service-session for the user and resource constraint for the network, specifically under heavy network load conditions and/or congestions.
Additionally, existing charging techniques (static or dynamic) may not provide for determination of charging rate commensurate with the delivered service quality. For example, service quality degradation or disruption may happen due to network faults or congestion, and/or due to overloading during the session. However, a user of a service-session may be overcharged in case of service interruptions or degraded service quality. Moreover, existing dynamic charging techniques are provided for a particular network (i.e., perform dynamic charging for intra-network sessions only), and may not perform dynamic charging across network segments. However, typically service end-points may span across multiple networks (different operators, different technologies, different geographies, etc.). In such a scenario, there may be no mechanism of dynamic determination of charging rate on the combined end-to-end network for the particular service-session. As a consequence, operator(s) catering such cross-network service-session may end up under or over charging for the delivered service quality.
In other words, existing techniques fail to provide appropriate amount proposed to be charged for the projected service consumption for a session spanning across multiple networks by taking into consideration desired service quality.